diff --git a/README.md b/README.md
index 1b1f02d..c0139b3 100644
--- a/README.md
+++ b/README.md
@@ -3,20 +3,12 @@ Implementation of [Equivariant Flow Matching for Molecule Conformer Generation](
 
 ET-Flow is a state-of-the-art generative model for generating small molecule conformations using equivariant transformers and flow matching.
 
-### Install Etflow
+### Install ET-flow
 We are now available on PyPI. Easily install the package using the following command:
 ```bash
 pip install etflow
 ```
 
-### Setup dev Environment
-Run the following commands to setup the environment:
-```bash
-conda env create -n etflow -f env.yml
-conda activate etflow
-# to install the etflow package
-python3 -m pip install -e .
-```
 
 ### Generating Conformations for Custom Smiles
 We have a sample notebook ([generate_confs.ipynb](generate_confs.ipynb)) to generate conformations for custom smiles input. One needs to pass the config and corresponding checkpoint path in order as additional inputs.
@@ -34,6 +26,15 @@ We currently support the following configurations and checkpoint:
 - `qm9-o3`
 - `drugs-so3`
 
+### Setup Dev Environment
+Run the following commands to setup the environment:
+```bash
+conda env create -n etflow -f env.yml
+conda activate etflow
+# to install the etflow package
+python3 -m pip install -e .
+```
+
 ### Preprocessing Data
 To pre-process the data, perform the following steps,
 1. Download the raw GEOM data and unzip the raw data using the following commands,
diff --git a/etflow/commons/covmat.py b/etflow/commons/covmat.py
index a021b67..b2345b8 100644
--- a/etflow/commons/covmat.py
+++ b/etflow/commons/covmat.py
@@ -29,6 +29,19 @@ def build_conformer(pos):
     return conformer
 
 
+def set_multiple_rdmol_positions(rdkit_mol, pos):
+    """
+    Args:
+        rdkit_mol:  An `rdkit.Chem.rdchem.Mol` object.
+        pos: (n, N_atoms, 3)
+    """
+    mol = deepcopy(rdkit_mol)
+    for conf_pos in pos:
+        conformer = build_conformer(conf_pos)
+        mol.AddConformer(conformer)
+    return mol
+
+
 def set_rdmol_positions(rdkit_mol, pos):
     """
     Args:
diff --git a/etflow/models/model.py b/etflow/models/model.py
index b7160e3..054e77a 100644
--- a/etflow/models/model.py
+++ b/etflow/models/model.py
@@ -1,4 +1,3 @@
-from copy import deepcopy
 from typing import Any, Dict, List, Optional, TypeVar
 
 import numpy as np
@@ -8,7 +7,7 @@
 from torch_geometric.data import Batch
 
 from etflow.commons.configs import CONFIG_DICT
-from etflow.commons.covmat import set_rdmol_positions
+from etflow.commons.covmat import set_multiple_rdmol_positions
 from etflow.commons.featurization import MoleculeFeaturizer, get_mol_from_smiles
 from etflow.commons.utils import signed_volume
 from etflow.models.base import BaseModel
@@ -706,13 +705,9 @@ def sample(
             )
             if as_mol:
                 mol = get_mol_from_smiles(smile)
-                set_rdmol_positions(mol, pos[0])
-                mols = []
-                for i in range(num_samples):
-                    copied_mol = deepcopy(mol)
-                    set_rdmol_positions(copied_mol, pos[i])
-                    mols.append(copied_mol)
-                data[smile] = mols
+                data[smile] = set_multiple_rdmol_positions(mol, pos)
+            else:
+                data[smile] = pos
         return data
 
 
diff --git a/tutorial.ipynb b/tutorial.ipynb
index 6a27d3b..0cbc2b1 100644
--- a/tutorial.ipynb
+++ b/tutorial.ipynb
@@ -13,7 +13,7 @@
       "  This is the list of implemented interpolations {self.__interpolation_types__}.\\n\"\"\"\n",
       "/Users/cristian.gabellini/Desktop/workspace/ETFlow/etflow/models/model.py:176: SyntaxWarning: invalid escape sequence '\\I'\n",
       "  This is the list of implemented interpolations {self.__path_types__}.\\n\"\"\"\n",
-      "\u001b[32m2024-12-12 00:37:56.328\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36metflow.utils\u001b[0m:\u001b[36minstantiate_model\u001b[0m:\u001b[36m137\u001b[0m - \u001b[1mLoading BaseFlow with args: {'network_type': 'TorchMDDynamics', 'hidden_channels': 160, 'num_layers': 20, 'num_rbf': 64, 'rbf_type': 'expnorm', 'trainable_rbf': True, 'activation': 'silu', 'neighbor_embedding': True, 'cutoff_lower': 0.0, 'cutoff_upper': 10.0, 'max_z': 100, 'node_attr_dim': 10, 'edge_attr_dim': 1, 'attn_activation': 'silu', 'num_heads': 8, 'distance_influence': 'both', 'reduce_op': 'sum', 'qk_norm': True, 'so3_equivariant': False, 'clip_during_norm': True, 'parity_switch': 'post_hoc', 'output_layer_norm': False, 'sigma': 0.1, 'prior_type': 'harmonic', 'interpolation_type': 'linear', 'optimizer_type': 'AdamW', 'lr': 0.0008, 'weight_decay': 1e-08, 'lr_scheduler_type': 'CosineAnnealingWarmupRestarts', 'first_cycle_steps': 375000, 'cycle_mult': 1.0, 'max_lr': 0.0005, 'min_lr': 1e-08, 'warmup_steps': 0, 'gamma': 0.05, 'last_epoch': -1, 'lr_scheduler_monitor': 'val/loss', 'lr_scheduler_interval': 'step', 'lr_scheduler_frequency': 1}\u001b[0m\n"
+      "\u001b[32m2024-12-15 20:33:45.785\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36metflow.utils\u001b[0m:\u001b[36minstantiate_model\u001b[0m:\u001b[36m137\u001b[0m - \u001b[1mLoading BaseFlow with args: {'network_type': 'TorchMDDynamics', 'hidden_channels': 160, 'num_layers': 20, 'num_rbf': 64, 'rbf_type': 'expnorm', 'trainable_rbf': True, 'activation': 'silu', 'neighbor_embedding': True, 'cutoff_lower': 0.0, 'cutoff_upper': 10.0, 'max_z': 100, 'node_attr_dim': 10, 'edge_attr_dim': 1, 'attn_activation': 'silu', 'num_heads': 8, 'distance_influence': 'both', 'reduce_op': 'sum', 'qk_norm': True, 'so3_equivariant': False, 'clip_during_norm': True, 'parity_switch': 'post_hoc', 'output_layer_norm': False, 'sigma': 0.1, 'prior_type': 'harmonic', 'interpolation_type': 'linear', 'optimizer_type': 'AdamW', 'lr': 0.0008, 'weight_decay': 1e-08, 'lr_scheduler_type': 'CosineAnnealingWarmupRestarts', 'first_cycle_steps': 375000, 'cycle_mult': 1.0, 'max_lr': 0.0005, 'min_lr': 1e-08, 'warmup_steps': 0, 'gamma': 0.05, 'last_epoch': -1, 'lr_scheduler_monitor': 'val/loss', 'lr_scheduler_interval': 'step', 'lr_scheduler_frequency': 1}\u001b[0m\n"
      ]
     },
     {
@@ -21,7 +21,7 @@
      "output_type": "stream",
      "text": [
       "Loading drugs-o3 from config\n",
-      "Checkpoint found at cache/drugs-o3.ckpt\n",
+      "Checkpoint found at /Users/cristian.gabellini/Desktop/workspace/ETFlow/cache/drugs-o3.ckpt\n",
       "Device cuda not found. Using cpu instead\n"
      ]
     },
@@ -30,36 +30,483 @@
      "output_type": "stream",
      "text": [
       "<unknown>:472: SyntaxWarning: invalid escape sequence '\\m'\n",
-      "/Users/cristian.gabellini/Desktop/workspace/ETFlow/etflow/models/model.py:214: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "/Users/cristian.gabellini/Desktop/workspace/ETFlow/etflow/models/model.py:215: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
       "  checkpoint = torch.load(checkpoint_path, map_location=device)\n",
       "Seed set to 42\n",
       "/Users/cristian.gabellini/Desktop/workspace/ETFlow/etflow/commons/utils.py:177: UserWarning: torch.sparse.SparseTensor(indices, values, shape, *, device=) is deprecated.  Please use torch.sparse_coo_tensor(indices, values, shape, dtype=, device=). (Triggered internally at /Users/runner/work/_temp/anaconda/conda-bld/pytorch_1724788636709/work/torch/csrc/utils/tensor_new.cpp:643.)\n",
       "  bgraph_adj = torch.sparse.LongTensor(edge_index, edge_type, torch.Size([N, N]))\n"
      ]
+    }
+   ],
+   "source": [
+    "from etflow import BaseFlow\n",
+    "model=BaseFlow.from_default(model=\"drugs-o3\", cache=\"/Users/cristian.gabellini/Desktop/workspace/ETFlow/cache/\")\n",
+    "smiles=model.predict(['CN1C=NC2=C1C(=O)N(C(=O)N2C)C'], num_samples=3, as_mol=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from etflow.commons.covmat import set_rdmol_positions, build_conformer\n",
+    "from etflow.commons.featurization import MoleculeFeaturizer, get_mol_from_smiles\n",
+    "mol = get_mol_from_smiles('CN1C=NC2=C1C(=O)N(C(=O)N2C)C')\n",
+    "mol2=set_rdmol_positions(mol,smiles['CN1C=NC2=C1C(=O)N(C(=O)N2C)C'][0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from copy import deepcopy\n",
+    "def set_rdmol_positions(rdkit_mol, pos):\n",
+    "    mol = deepcopy(rdkit_mol)\n",
+    "    for conf in pos:\n",
+    "        conformer = build_conformer(pos)\n",
+    "        mol.AddConformer(conformer)\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(smiles['CN1C=NC2=C1C(=O)N(C(=O)N2C)C'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
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+       "\tviewer_17343237043149729.setBackgroundColor(\"0xeeeeee\");\n",
+       "\tviewer_17343237043149729.zoomTo();\n",
+       "viewer_17343237043149729.render();\n",
+       "});\n",
+       "</script>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "data": {
+      "image/png": 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+       "' fill='#000000'/>\n",
+       "</svg>\n"
+      ],
+      "text/html": [],
       "text/plain": [
-       "{'CN1C=NC2=C1C(=O)N(C(=O)N2C)C': [<rdkit.Chem.rdchem.Mol at 0x28069b220>,\n",
-       "  <rdkit.Chem.rdchem.Mol at 0x28069b370>,\n",
-       "  <rdkit.Chem.rdchem.Mol at 0x28069b3e0>]}"
+       "<rdkit.Chem.rdchem.Mol at 0x17b39ae30>"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from etflow import BaseFlow\n",
-    "model=BaseFlow.from_default(model=\"drugs-o3\")\n",
-    "model.predict(['CN1C=NC2=C1C(=O)N(C(=O)N2C)C'], num_samples=3, as_mol=True)"
+    "import py3Dmol # might have to pip install this for viz\n",
+    "from rdkit.Chem.Draw import IPythonConsole\n",
+    "IPythonConsole.molSize = (600, 600)   # Change image size\n",
+    "IPythonConsole.ipython_useSVG = True  # Change output to SVG\n",
+    "IPythonConsole.ipython_3d = True\n",
+    "mol2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "smiles"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "etflow",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },